{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Day02 认识Figure\n",
    "\n",
    "　　正式进入`matplotlib`系统性学习的第一天，我们首先来认识`matplotlib`中的`Figure`对象。\n",
    "  \n",
    "　　就像现实中绘画需要画纸、画板一样，我们在`matplotlib`中进行绘图，就需要先有一个画布，即今天要介绍的`Figure`对象。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-13T10:16:38.542137Z",
     "iopub.status.busy": "2020-10-13T10:16:38.541139Z",
     "iopub.status.idle": "2020-10-13T10:16:38.921156Z",
     "shell.execute_reply": "2020-10-13T10:16:38.921156Z",
     "shell.execute_reply.started": "2020-10-13T10:16:38.542137Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "matplotlib版本： 3.0.3\n"
     ]
    }
   ],
   "source": [
    "import matplotlib;print('matplotlib版本：', matplotlib.__version__)\n",
    "import matplotlib.pyplot as plt # 从matplotlib中导入我们最经常使用的pyplot子模块"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　像上面一样从`matplotlib`导入`pyplot`子模块之后，我们来创建`Figure`对象。\n",
    "  \n",
    "　　在`matplotlib`中开辟`Figure`画布对象的方式有若干种，我们只需要掌握下面这种基于`plt.subplots()`的方式即可，它返回两个对象，第一个位置上的就是我们的`Figure`对象，第二个位置对应的对象我们之后的日程再具体介绍："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-13T10:16:38.922118Z",
     "iopub.status.busy": "2020-10-13T10:16:38.922118Z",
     "iopub.status.idle": "2020-10-13T10:16:39.025844Z",
     "shell.execute_reply": "2020-10-13T10:16:39.025844Z",
     "shell.execute_reply.started": "2020-10-13T10:16:38.922118Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-13T10:16:39.026923Z",
     "iopub.status.busy": "2020-10-13T10:16:39.026923Z",
     "iopub.status.idle": "2020-10-13T10:16:39.033831Z",
     "shell.execute_reply": "2020-10-13T10:16:39.032825Z",
     "shell.execute_reply.started": "2020-10-13T10:16:39.026923Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matplotlib.figure.Figure"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看fig变量的类型\n",
    "type(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　而在利用`plt.subplots()`来创建`Figure`对象时，我们可以传入一些参数来预设一些个性化参数，常见的有以下几种：\n",
    "\n",
    "> **figsize**：二元组，用于定义画板的宽与高，格式为`(宽, 高)`，单位是英寸<br>\n",
    "> **facecolor**：字符串或numpy格式的RGB、RGBA数组，用于设置画布的背景色，具体的色彩格式之后的日程会详细介绍<br>\n",
    "> **edgecolor**：字符串或numpy格式的RGB、RGBA数组，用于设置画布边缘轮廓色彩<br>\n",
    "> **linewidth**：数值型，用于设置画板边缘轮廓宽度<br>\n",
    "> **dpi**：即每英寸像素点数（*Dot Per Inch*），用于控制导出图像的精细程度<br>\n",
    "\n",
    "　　下面我们来实际演示这些参数的效果："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2020-10-13T10:16:39.034818Z",
     "iopub.status.busy": "2020-10-13T10:16:39.033831Z",
     "iopub.status.idle": "2020-10-13T10:16:39.278166Z",
     "shell.execute_reply": "2020-10-13T10:16:39.278166Z",
     "shell.execute_reply.started": "2020-10-13T10:16:39.034818Z"
    }
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 2500x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt \n",
    "fig, ax = plt.subplots(figsize=(5, 3), facecolor='red',\n",
    "                       edgecolor='black', linewidth=10,\n",
    "                       dpi=500)\n",
    "\n",
    "# 利用Figure对象的savefig方法即可向外写出图像文件\n",
    "fig.savefig('demo.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "　　查看本地导出的图片文件的详细信息可以看到对应的**分辨率**其实就是前面设置的宽与高再乘以`dpi`的结果：\n",
    "  \n",
    "<center><img src='imgs/图1.png'></img></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 课后测验：\n",
    "\n",
    "　　相信通过上面的内容，你已经对如何在`matplotlib`中创建`Figure`画布对象有了了解，而今天的课后测验题目就是请你利用今天所学的知识，创建一个最终导出图片文件分辨率为**(2500, 1500)**的`Figure`对象（具体的figsize与dpi参数的设置由你自己决定），并且使用到上面介绍的所有参数（你可以随意的设置其余的参数值，对于常见颜色直接输入英文名即可如：`黄色：'yellow'`）\n",
    "\n",
    "* 请在今天对应的帖子下回复你的文字代码以及导出图片的截图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {},
    "version_major": 2,
    "version_minor": 0
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
